Image Analysis Computer Vision

Course: Artificial Intelligence

Structural unit: Faculty of Computer Science and Cybernetics

Title
Image Analysis Computer Vision
Code
ННД.17.
Module type
Обов’язкова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
3 Semester
Number of ECTS credits allocated
5
Learning outcomes
1. Know the mathematical formulation of image processing problems, approaches to solving computer vision problems, tools of machine learning methods, basic concepts and principles of artificial neural networks. 2. Be able to analyze the problem of image processing and recognition to choose the best method to solve it. Be able to formulate professional tasks in the language of machine learning and on the basis of formulations of basic tasks of computer vision.
Form of study
Prerequisites and co-requisites
1. Know: basic disciplines - "Mathematical Analysis", "Linear Algebra and Geometry", "Probability Theory and Mathematical Statistics", "Programming", "Computational Geometry and Computer Graphics", "Algorithms and Data Structures", "Modern methods of machine learning "," Current issues of "Data Mining". 2. Be able to: develop, analyze and apply algorithms and software to solve application problems using modern methods of program development.
Course content
This educational program is a basic discipline of leading domestic and foreign universities specializing in information and computer technology, and, in particular, in the field of artificial intelligence. The purpose and objectives of the discipline is to get acquainted with one of the main scientific areas in the field of artificial intelligence and mastering the technology of solving a wide range of engineering and scientific problems (including image recognition, computer vision, intelligent control) using modern mathematical methods, approaches and algorithms and, in particular, machine learning methods.
Recommended or required reading and other learning resources/tools
6. Richard Szeliski. Computer Vision: Algorithms and Applications, Springer, 2010 7.Stephen Marsland. Machine Learning: An Algorithmic Perspective, 452 р., 2015. 8.Christopher M Bishop. Pattern recognition. Machine Learning, 128 p., 2006. 9.Ethem Alpaydin. Introduction To Machine Learning, 584 p., 2009. 10.Tom M. Mitchell. Machine Learning [http://www.cs.cmu.edu/~tom/mlbook.html] 11.Yaser S. Abu-Mostafa. Learning from data, 215 p., 2017 12.Alex Smola. Introduction to Machine Learning, 234 p., 2008. 13.Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction, 764 p., 2008.
Planned learning activities and teaching methods
Lecture, laboratory work, individual work.
Assessment methods and criteria
Control work. Defense of laboratory work, exam.
Language of instruction
Ukrainian language

Lecturers

This discipline is taught by the following teachers

Departments

The following departments are involved in teaching the above discipline